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- 3Compressed sensing
- 1DOA estimation
- 1Multidimensional signal processing
- 1Random matrix
- 1Restricted isometry property
The advent of compressed sensing provides a new way to sample and compress signals. In this thesis, a parallel compressed sensing architecture is proposed, which samples a two-dimensional reshaped multidimensional signal column by column using the same sensing matrix. Compared to architectures...
Parameter Estimation in Low-Rank Models from Small Sample Size and Undersampled Data: DOA and Spectrum EstimationDownload
In estimation theory, a set of parameters are estimated from a finite number of measurements (samples). In general, the quality of estimation degrades as the number of samples is reduced. In this thesis, the problem of parameter estimation in low-rank models from a small number of samples is...
The Strong Restricted Isometry Property of Sub-Gaussian Matrices and the Erasure Robustness Property of Gaussian Random FramesDownload
In this thesis we will study the robustness property of sub-gaussian random matrices. We first show that the nearly isometry property will still hold with high probability if we erase a certain portion of rows from a sub-gaussian matrix, and we will estimate the erasure ratio with a given small...